In SISR, the idea of cycle consistency has also been widely discussed. Given the LR images domain \(X\)  and the HR images domain \(Y\) , we not only learn the mapping from LR to HR but also the backward process. Researchers have shown that learning how to do image degradation first without paired data can help generate more realistic images [1]}. In CinCGAN [2]}, a cycle in cycle network is proposed, where the noisy and blurry input is mapped to a noise-free LR domain firstly and then upsampled with a pre-trained model and finally mapped to the HR domain. In DRN [3]}, the mapping from HR to LR images is learned to estimate the down-sampling kernel and reconstruct LR images, which forms a closed-loop to provide additional supervision. DRN also gives us a novel approach in unsupervised learning SR, where the deep network is trained with both paired and unpaired data.
